Other domain-shift benchmarks include the WILDSbenchmark [1]}, which itself is a collection of 10 mostly image and text datasets for evaluating out-of-distribution performance. For instance, WILDS includes the Camelyon17 histopathology dataset [2]}, where the task is image classification but where the test set consists of data collected from a hospital different from that of the training set.
Other prior work (e.g., [3]}, [4]}, [5]}) evaluates out-of-distribution detection for image classifiers trained on CIFAR-10/100 by testing on various other image datasets, but does not account for the problem of discriminating between out-of-distribution inputs that are out-of-domain versus in-domain.
